This paper presents a detailed BC, NOx and SO2 emission inventory for ships in the Arctic in 2012 based on satellite AIS data, ship engine power functions and technology stratified emission factors. Emission projections are presented for the years 2020, 2030 and 2050. Furthermore, the BC, SO2 and O3 concentrations and the deposition of BC are calculated for 2012 and for two arctic shipping scenarios – with or without arctic diversion routes due to a possible polar sea ice extent in the future.
In 2012, the largest shares of Arctic ships emissions are calculated for fishing ships (45% for BC, 38% for NOx, 23% for SO2) followed by passenger ships (20%, 17%, 25%), tankers (9%, 13%, 15%), general cargo (8%, 11%, 12%) and container ships (5%, 7%, 8%). In 2050, without arctic diversion routes, the total emissions of BC, NOx and SO2 are expected to change by +16%, −32% and −63%, respectively, compared to 2012. The results for fishing ships are the least certain, caused by a less precise engine power – sailing speed relation.
The calculated BC, SO2, and O3 surface concentrations and BC deposition contributions from ships are low as a mean for the whole Arctic in 2012, but locally BC additional contributions reach up to 20% around Iceland, and high additional contributions (100–300%) are calculated in some sea areas for SO2. In 2050, the arctic diversion routes highly influence the calculated surface concentrations and the deposition of BC in the Arctic. During summertime navigation contributions become very visible for BC (>80%) and SO2 (>1000%) along the arctic diversion routes, while the O3 (>10%) and BC deposition (>5%) additional contributions, respectively, get highest over the ocean east of Greenland and in the High Arctic.
The geospatial ship type specific emission results presented in this paper have increased the accuracy of the emission inventories for ships in the Arctic. The methodology can be used to estimate shipping emissions in other regions of the world, and hence may serve as an input for other researchers and policy makers working in this field.
Port selection is of vital importance for both port operators and shipping lines. In this contribution, an Automatic Identification System (AIS) big data approach is developed. This approach allows identifying container ships using only AIS data without the need for supplementary information from commercial databases. This approach is applied to investigate the port selection statistics of container ships between Shanghai and Ningbo Zhoushan Port, two of the largest ports in the world in terms of calling frequency, to generate practical insights. Results show that: i) the ratios among large ships, medium ships and small ships of these two ports are both approximately 1: 4: 5; ii) these two ports both have an exclusive (i.e., more feeder ports covered in geographical coverage) and intensive (i.e., more feeder ships deployed in shipping service frequency) collection and distribution network mainly consisting of small ships, but that of Shanghai is more intensive; iii) in terms of ultra-large ships over 380 m, Shanghai has accommodated an extra 18.5% compared to that of Ningbo Zhoushan, this indicates Shanghai's attraction for such vessels in global fleet deployment; iv) the feeder network between Shanghai and Ningbo Zhoushan is weak, and their relationship is actually in competition; v) Ningbo Zhoushan could offer more choices for ultra-large container ships (over 380 m), which implies its greater potential in future port competition; vi) when the depth of channels and berths is sufficient, the distance to hinterland and the convenience of a collection and distribution network begin to get more important in port selection. The empirical findings unveil the decision-making of container lines, competition between ports and implications for shipping policy.